Âé¶¹ÒùÔº


Deep mining of teaching assessment data to measure quality of online education

online class
Credit: Pixabay/CC0 Public Domain

Online education is now ubiquitous and in recent years has fundamentally changed the way many people learn. Various platforms have opened up access to knowledge for millions of people. However, there remains an ongoing challenge: how to accurately measure and enhance the quality of teaching in these digital spaces.

Conventional evaluation tools focus on and student satisfaction surveys. However, these often overlook the students' emotional experience of the course. Research in the International Journal of Information and Communication Technology proposes a new solution that could change the way online teaching is assessed, getting closer to the heart of emotional matters.

The new work by Ruiting Bai of Puyang Medical College in Puyang, China, introduces the EduSent-Dig model, which can carry out advanced sentiment analysis and use big data techniques to evaluate teaching quality. By analyzing the student given in their course feedback, the model can extract the nuances of online teaching that work most effectively.

Rather than flagging the feedback as simply "positive" or "negative," EduSent-Dig identifies specific emotional undercurrents such as joy, frustration, or surprise. It does so by using such as Bi-LSTM, a deep learning framework, and Word2Vec, which converts words into numerical representations for computational analysis.

The study reveals that emotional experiences are not just peripheral to learning; they are central to it. How students feel about their coursework directly affects their motivation, engagement, and whether they complete a course. As such, the new model is identifying and interpreting sentiment accurately, and can provide educators and course designers with insights into how to improve their educational offering.

Moreover, real-time sentiment analysis undertaken as a course progresses might even allow teachers to fine tune their dynamically, tailoring lessons to student needs on an ad hoc basis. This could transform the way courses are designed and how they are developed as the students progress through them. All in, the insights could foster a more empathetic and effective learning environment.

More information: Ruiting Bai, Big data-driven deep mining of online teaching assessment data under affective factor conditions, International Journal of Information and Communication Technology (2024).

Provided by Inderscience

Citation: Deep mining of teaching assessment data to measure quality of online education (2025, January 13) retrieved 20 August 2025 from /news/2025-01-deep-quality-online.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Explore further

Transforming education with virtual reality and artificial intelligence

0 shares

Feedback to editors